Many applications in industry, consumer devices and healthcare involve the processing and transformation of raw information signals into useful or improved outputs which can be interpreted by users or passed on for further analysis, processing and decision making. These signals are often both high-dimensional and high-velocity, for example vehicle camera video, Magnetoencephalograph (MEG), and sensors embedded in large machines.
Such applications traditionally require the manufacturer to develop complex hardware and software systems in order to process the raw inputs from sensors and generate outputs which are valuable to the user. This incurs significant research, development, testing and maintenance costs, which adds to the expense and running costs of the system. This is exacerbated by the need to largely rewrite or re-engineer such systems when new components are added, the sensor technology changes, or the system is repurposed for a new application domain.
Traditional systems of this kind also usually require the employment of experts at every stage, from the identifying of requirements, to vendor selection and purchasing, installation, integration, maintenance and most importantly operation. Such experts may be rare and costly, and are diverted from other productive tasks in an organisation. All this complication and cost reduces the scale at which new analytical and diagnostic systems can be adopted, and thus significantly reduces the benefits available to industry, consumers and health practitioners of advances in sensor technology.
In answer to this, some progress has been made by using machine learning to replace or augment traditional hand-engineered and hand-coded componentry in such systems. Unfortunately, most such machine learning methods are ill-suited to the particular conditions which exist in the domains under discussion, for a variety of reasons.